Lupus data mock DE results
Milan Malfait
April 04, 2023
library(here)
library(tidyverse)
library(cowplot)
library(GGally)
theme_set(theme_light())
library(scuttle)
library(scater)
library(kableExtra)
library(SCandwichCompanion)Setup
## Directory setup
here_root <- "benchmarks/lupus"
here::i_am(file.path(here_root, "analysis/lupus-mock-results.Rmd"))
#> here() starts at /Users/jg/Desktop/PhD/DD_project/DD_git
res_dir <- here::here(here_root, "results")
fig_dir <- here::here(here_root, "figures")- Using
methods: muscat, qbGLM_offset_squeeze, bGLM, qbGLM, qbGLM_offset - Using
celltypes: B_mem, ncM
Load results
res_files <- map(celltypes, ~ get_mock_res_files(
dataset = "lupus",
methods = methods, celltype = .x
)) %>%
set_names(celltypes)
res_list <- map_depth(res_files, 2, readRDS)
## Do some wrangling when using 'combined' celltypes
if (combined) {
## Only 1 top-level entry + transpose so that celltypes are the top-level
res_list <- transpose(res_list[[1]])
}
## Check for expected data structure
stopifnot(unlist(map_depth(res_list, 3, ~ is.data.frame(.x$results))))Load SCE objects
data_files <- map(celltypes, ~ get_SCE_files(
dataset = "lupus", which = "mock_replicates",
celltype = .x
)) %>%
set_names(celltypes)
sce_objects <- map(data_files, readRDS)Data overview
- The results were generated from 5 mock replicates
- Each replicate was generated by randomly splitting the subjects in two mock groups
- No sub-sampling of cells per patient was performed for this data
map_dfr(sce_objects,
~ map_dfr(.x, function(x) c(nrows = nrow(x), ncols = ncol(x)),
.id = "replicate"
),
.id = "celltypes"
)Subjects are divided across mock groups as follows:
map(sce_objects[[1]], function(x) table(x$ind_cov, x$mock_group))
#> $replicate_1
#>
#> A B
#> IGTB141_IGTB141 144 0
#> IGTB143_IGTB143 84 0
#> IGTB195_IGTB195 0 119
#> IGTB469_IGTB469 89 0
#> IGTB498_IGTB498 208 0
#> IGTB508_IGTB508 278 0
#> IGTB514_IGTB514 373 0
#> IGTB645_IGTB645 123 0
#> IGTB670_IGTB670 0 64
#> IGTB826_IGTB826 0 511
#> IGTB884_IGTB884 128 0
#> IGTB986_IGTB986 137 0
#> IGTB1372_IGTB1372 0 136
#> IGTB1506_IGTB1506 0 83
#> IGTB1539_IGTB1539 305 0
#> IGTB1575_IGTB1575 0 164
#> IGTB1650_IGTB1650 0 137
#> IGTB1762_IGTB1762 176 0
#> IGTB1768_IGTB1768 107 0
#> IGTB1789_IGTB1789 0 341
#> IGTB1793_IGTB1793 0 119
#> IGTB1814_IGTB1814 485 0
#> IGTB1815_IGTB1815 0 168
#> IGTB1826_IGTB1826 0 70
#> IGTB1827_IGTB1827 122 0
#> IGTB1828_IGTB1828 356 0
#> IGTB1840_IGTB1840 0 328
#> IGTB1844_IGTB1844 0 177
#> IGTB1871_IGTB1871 0 241
#> IGTB1895_IGTB1895 231 0
#> IGTB1901_IGTB1901 150 0
#> IGTB1906_IGTB1906 0 185
#> IGTB1908_IGTB1908 139 0
#> IGTB1913_IGTB1913 156 0
#> IGTB1916_IGTB1916 0 183
#> IGTB1921_IGTB1921 0 181
#> IGTB1952_IGTB1952 199 0
#> IGTB1966_IGTB1966 0 567
#> IGTB1974_IGTB1974 0 129
#> IGTB1982_IGTB1982 0 244
#> IGTB1987_IGTB1987 0 145
#> IGTB1996_IGTB1996 88 0
#> IGTB2007_IGTB2007 149 0
#> IGTB2065_IGTB2065 0 205
#>
#> $replicate_2
#>
#> A B
#> IGTB141_IGTB141 144 0
#> IGTB143_IGTB143 0 84
#> IGTB195_IGTB195 119 0
#> IGTB469_IGTB469 89 0
#> IGTB498_IGTB498 0 208
#> IGTB508_IGTB508 0 278
#> IGTB514_IGTB514 0 373
#> IGTB645_IGTB645 0 123
#> IGTB670_IGTB670 0 64
#> IGTB826_IGTB826 511 0
#> IGTB884_IGTB884 0 128
#> IGTB986_IGTB986 137 0
#> IGTB1372_IGTB1372 0 136
#> IGTB1506_IGTB1506 0 83
#> IGTB1539_IGTB1539 305 0
#> IGTB1575_IGTB1575 164 0
#> IGTB1650_IGTB1650 137 0
#> IGTB1762_IGTB1762 0 176
#> IGTB1768_IGTB1768 107 0
#> IGTB1789_IGTB1789 0 341
#> IGTB1793_IGTB1793 0 119
#> IGTB1814_IGTB1814 485 0
#> IGTB1815_IGTB1815 168 0
#> IGTB1826_IGTB1826 70 0
#> IGTB1827_IGTB1827 122 0
#> IGTB1828_IGTB1828 356 0
#> IGTB1840_IGTB1840 0 328
#> IGTB1844_IGTB1844 0 177
#> IGTB1871_IGTB1871 241 0
#> IGTB1895_IGTB1895 0 231
#> IGTB1901_IGTB1901 150 0
#> IGTB1906_IGTB1906 185 0
#> IGTB1908_IGTB1908 0 139
#> IGTB1913_IGTB1913 0 156
#> IGTB1916_IGTB1916 0 183
#> IGTB1921_IGTB1921 181 0
#> IGTB1952_IGTB1952 199 0
#> IGTB1966_IGTB1966 0 567
#> IGTB1974_IGTB1974 0 129
#> IGTB1982_IGTB1982 244 0
#> IGTB1987_IGTB1987 0 145
#> IGTB1996_IGTB1996 0 88
#> IGTB2007_IGTB2007 149 0
#> IGTB2065_IGTB2065 205 0
#>
#> $replicate_3
#>
#> A B
#> IGTB141_IGTB141 0 144
#> IGTB143_IGTB143 0 84
#> IGTB195_IGTB195 0 119
#> IGTB469_IGTB469 89 0
#> IGTB498_IGTB498 0 208
#> IGTB508_IGTB508 0 278
#> IGTB514_IGTB514 0 373
#> IGTB645_IGTB645 0 123
#> IGTB670_IGTB670 64 0
#> IGTB826_IGTB826 511 0
#> IGTB884_IGTB884 128 0
#> IGTB986_IGTB986 0 137
#> IGTB1372_IGTB1372 136 0
#> IGTB1506_IGTB1506 83 0
#> IGTB1539_IGTB1539 305 0
#> IGTB1575_IGTB1575 164 0
#> IGTB1650_IGTB1650 0 137
#> IGTB1762_IGTB1762 0 176
#> IGTB1768_IGTB1768 0 107
#> IGTB1789_IGTB1789 341 0
#> IGTB1793_IGTB1793 119 0
#> IGTB1814_IGTB1814 485 0
#> IGTB1815_IGTB1815 0 168
#> IGTB1826_IGTB1826 0 70
#> IGTB1827_IGTB1827 122 0
#> IGTB1828_IGTB1828 356 0
#> IGTB1840_IGTB1840 328 0
#> IGTB1844_IGTB1844 177 0
#> IGTB1871_IGTB1871 0 241
#> IGTB1895_IGTB1895 231 0
#> IGTB1901_IGTB1901 150 0
#> IGTB1906_IGTB1906 185 0
#> IGTB1908_IGTB1908 139 0
#> IGTB1913_IGTB1913 0 156
#> IGTB1916_IGTB1916 0 183
#> IGTB1921_IGTB1921 181 0
#> IGTB1952_IGTB1952 0 199
#> IGTB1966_IGTB1966 567 0
#> IGTB1974_IGTB1974 129 0
#> IGTB1982_IGTB1982 0 244
#> IGTB1987_IGTB1987 0 145
#> IGTB1996_IGTB1996 0 88
#> IGTB2007_IGTB2007 149 0
#> IGTB2065_IGTB2065 0 205
#>
#> $replicate_4
#>
#> A B
#> IGTB141_IGTB141 0 144
#> IGTB143_IGTB143 84 0
#> IGTB195_IGTB195 119 0
#> IGTB469_IGTB469 0 89
#> IGTB498_IGTB498 0 208
#> IGTB508_IGTB508 0 278
#> IGTB514_IGTB514 373 0
#> IGTB645_IGTB645 0 123
#> IGTB670_IGTB670 64 0
#> IGTB826_IGTB826 0 511
#> IGTB884_IGTB884 128 0
#> IGTB986_IGTB986 0 137
#> IGTB1372_IGTB1372 0 136
#> IGTB1506_IGTB1506 0 83
#> IGTB1539_IGTB1539 305 0
#> IGTB1575_IGTB1575 164 0
#> IGTB1650_IGTB1650 137 0
#> IGTB1762_IGTB1762 0 176
#> IGTB1768_IGTB1768 107 0
#> IGTB1789_IGTB1789 341 0
#> IGTB1793_IGTB1793 0 119
#> IGTB1814_IGTB1814 0 485
#> IGTB1815_IGTB1815 168 0
#> IGTB1826_IGTB1826 0 70
#> IGTB1827_IGTB1827 0 122
#> IGTB1828_IGTB1828 0 356
#> IGTB1840_IGTB1840 0 328
#> IGTB1844_IGTB1844 0 177
#> IGTB1871_IGTB1871 0 241
#> IGTB1895_IGTB1895 0 231
#> IGTB1901_IGTB1901 150 0
#> IGTB1906_IGTB1906 185 0
#> IGTB1908_IGTB1908 139 0
#> IGTB1913_IGTB1913 156 0
#> IGTB1916_IGTB1916 183 0
#> IGTB1921_IGTB1921 181 0
#> IGTB1952_IGTB1952 0 199
#> IGTB1966_IGTB1966 567 0
#> IGTB1974_IGTB1974 0 129
#> IGTB1982_IGTB1982 0 244
#> IGTB1987_IGTB1987 0 145
#> IGTB1996_IGTB1996 88 0
#> IGTB2007_IGTB2007 149 0
#> IGTB2065_IGTB2065 205 0
#>
#> $replicate_5
#>
#> A B
#> IGTB141_IGTB141 0 144
#> IGTB143_IGTB143 0 84
#> IGTB195_IGTB195 0 119
#> IGTB469_IGTB469 89 0
#> IGTB498_IGTB498 208 0
#> IGTB508_IGTB508 0 278
#> IGTB514_IGTB514 373 0
#> IGTB645_IGTB645 0 123
#> IGTB670_IGTB670 64 0
#> IGTB826_IGTB826 511 0
#> IGTB884_IGTB884 0 128
#> IGTB986_IGTB986 137 0
#> IGTB1372_IGTB1372 136 0
#> IGTB1506_IGTB1506 0 83
#> IGTB1539_IGTB1539 305 0
#> IGTB1575_IGTB1575 164 0
#> IGTB1650_IGTB1650 137 0
#> IGTB1762_IGTB1762 176 0
#> IGTB1768_IGTB1768 0 107
#> IGTB1789_IGTB1789 341 0
#> IGTB1793_IGTB1793 0 119
#> IGTB1814_IGTB1814 0 485
#> IGTB1815_IGTB1815 0 168
#> IGTB1826_IGTB1826 70 0
#> IGTB1827_IGTB1827 0 122
#> IGTB1828_IGTB1828 0 356
#> IGTB1840_IGTB1840 0 328
#> IGTB1844_IGTB1844 177 0
#> IGTB1871_IGTB1871 0 241
#> IGTB1895_IGTB1895 231 0
#> IGTB1901_IGTB1901 150 0
#> IGTB1906_IGTB1906 0 185
#> IGTB1908_IGTB1908 0 139
#> IGTB1913_IGTB1913 0 156
#> IGTB1916_IGTB1916 183 0
#> IGTB1921_IGTB1921 181 0
#> IGTB1952_IGTB1952 0 199
#> IGTB1966_IGTB1966 567 0
#> IGTB1974_IGTB1974 0 129
#> IGTB1982_IGTB1982 0 244
#> IGTB1987_IGTB1987 145 0
#> IGTB1996_IGTB1996 0 88
#> IGTB2007_IGTB2007 149 0
#> IGTB2065_IGTB2065 205 0Extract results of interest
Runtimes
## Get runtimes for each celltype
runtimes <- map(res_list, ~ map_dfr(.x, get_runtimes, depth = 1, .id = "method"))P-values
res_tables <- map_depth(
res_list, 2,
~ get_aggregated_rep_tables(.x, depth = 1)
) %>%
map(combine_tables, .id = "method")Visualize results
Run times
runtime_plots <- imap(runtimes,
~ plot_run_times(.x, width = 0.2, height = 0) +
ggtitle(.y)
)B_mem
ncM
Number of significant genes for different FDR levels
n_sign_summaries <- map(res_tables, function(tmp) {
tmp %>%
group_by(replicate, method) %>%
summarise(
sum(FDR < 0.01),
sum(FDR < 0.05),
sum(FDR < 0.1),
.groups = "drop"
)
})B_mem
| method | sum(FDR < 0.01) | sum(FDR < 0.05) | sum(FDR < 0.1) |
|---|---|---|---|
| Replicate: 1 | |||
| bGLM | 1 | 1 | 2 |
| muscat | 0 | 0 | 0 |
| qbGLM | 1 | 1 | 2 |
| qbGLM_offset | 1 | 2 | 2 |
| qbGLM_offset_squeeze | 1 | 2 | 2 |
| Replicate: 2 | |||
| bGLM | 0 | 2 | 3 |
| muscat | 1 | 1 | 1 |
| qbGLM | 0 | 2 | 3 |
| qbGLM_offset | 0 | 2 | 4 |
| qbGLM_offset_squeeze | 0 | 2 | 4 |
| Replicate: 3 | |||
| bGLM | 0 | 0 | 3 |
| muscat | 0 | 1 | 1 |
| qbGLM | 0 | 0 | 3 |
| qbGLM_offset | 0 | 0 | 3 |
| qbGLM_offset_squeeze | 0 | 0 | 3 |
| Replicate: 4 | |||
| bGLM | 0 | 0 | 0 |
| muscat | 0 | 0 | 0 |
| qbGLM | 0 | 0 | 0 |
| qbGLM_offset | 0 | 0 | 0 |
| qbGLM_offset_squeeze | 0 | 0 | 0 |
| Replicate: 5 | |||
| bGLM | 0 | 0 | 0 |
| muscat | 0 | 0 | 0 |
| qbGLM | 0 | 0 | 0 |
| qbGLM_offset | 0 | 0 | 0 |
| qbGLM_offset_squeeze | 0 | 0 | 0 |
ncM
| method | sum(FDR < 0.01) | sum(FDR < 0.05) | sum(FDR < 0.1) |
|---|---|---|---|
| Replicate: 1 | |||
| bGLM | 0 | 1 | 1 |
| muscat | 0 | 0 | 0 |
| qbGLM | 0 | 1 | 1 |
| qbGLM_offset | 0 | 1 | 1 |
| qbGLM_offset_squeeze | 0 | 1 | 1 |
| Replicate: 2 | |||
| bGLM | 1 | 1 | 4 |
| muscat | 0 | 1 | 1 |
| qbGLM | 1 | 1 | 4 |
| qbGLM_offset | 1 | 1 | 3 |
| qbGLM_offset_squeeze | 1 | 1 | 3 |
| Replicate: 3 | |||
| bGLM | 1 | 1 | 1 |
| muscat | 0 | 1 | 1 |
| qbGLM | 1 | 1 | 1 |
| qbGLM_offset | 1 | 1 | 1 |
| qbGLM_offset_squeeze | 1 | 1 | 1 |
| Replicate: 4 | |||
| bGLM | 2 | 3 | 5 |
| muscat | 0 | 1 | 1 |
| qbGLM | 2 | 3 | 5 |
| qbGLM_offset | 0 | 1 | 3 |
| qbGLM_offset_squeeze | 0 | 1 | 3 |
| Replicate: 5 | |||
| bGLM | 1 | 1 | 1 |
| muscat | 0 | 0 | 0 |
| qbGLM | 1 | 1 | 1 |
| qbGLM_offset | 1 | 1 | 1 |
| qbGLM_offset_squeeze | 1 | 1 | 1 |
P-value distributions
pval_figs <- map(res_tables, pval_hist)B_mem
ncM
P-value distributions stratified by expression level
For each replicate, calculate average log-normalized counts, using
scuttle::calculateAverage().
## Calculate average counts for each replicate
avg_counts <- map_depth(sce_objects, 2,
~ calculateAverage(counts(.x))
)
## Split res_tables per replicate
res_tables_split <- map(res_tables, ~ split(.x, .x$replicate))
## Add average counts to results tables
## Note that for the 'combined' celltypes, the average counts will be the same
## for each celltype contrast
res_tables_split <- map2(
res_tables_split, avg_counts,
~ map2(.x, .y, function(res, avg_cnt) {
res$avg_count <- avg_cnt[res$gene]
res
})
)Make p-value histograms stratified by the average counts.
pval_hist_strat_plots <- map(res_tables_split,
~ imap(.x, function(y, replicate) {
replicate <- str_remove(replicate, "replicate_")
title <- paste("Replicate:", replicate)
pval_hist_strat(y, n_groups = 6) +
ggtitle(title)
})
)B_mem
ncM
Session info
Session info
#> [1] "2023-04-04 15:49:37 CEST"
#> Local: master /Users/jg/Desktop/PhD/DD_project/DD_git
#> Remote: master @ origin (git@github.com:statOmics/PseudoreplicationPaper.git)
#> Head: [9faa16f] 2022-11-28: Add README
#> ─ Session info ──────────────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.1.3 (2022-03-10)
#> os macOS Big Sur/Monterey 10.16
#> system x86_64, darwin17.0
#> ui X11
#> language (EN)
#> collate C
#> ctype UTF-8
#> tz Europe/Brussels
#> date 2023-04-04
#> pandoc 2.17.1.1 @ /Users/jg/opt/anaconda3/bin/ (via rmarkdown)
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